This is the section where you could put some basic description information about the site. You can also start to use the inline text feature and have a generic sentence that is automatically updated each time you enter a new dataset.
For instance, Catawba Creek was sampled 2 times for benthic macroinvertebrates and 14 times for ambient parameters between January 2016 and December 2016.
Alternatively, you could turn that information into a table to save space. Here are a few quick stats from the datasets.
| StationID | 2-CAT027.64 |
| Longitude | -80.02177 |
| Latitude | 37.42594 |
| StreamName | Catawba Creek |
| Location | Hogan Hollow Rd. (Rt. 737) |
| Median VSCI | 57.9 |
| Number of Samples | 2 |
And you can give a little interactive map if you’d like.
Here is a basic plot of Family level Virginia Stream Condition Index scores for the input dataset. The function the plot is built with can handle more samples if you format your data to match in input data template. You can also go in and alter the code to change SCI threshold to match your benthic cutoff.
Figure 1: Virginia Stream Condition Index (VSCI) for Catawba Creek. A score of less than or equal to 60 indicates an impaired benthic community. No replicate samples are represented in the dataset.
Depending on your audience, you can present the underlying metrics of your SCI and highlight potential metrics driving the overall score. For Virginia, we like to highlight the lowest two metrics. This is a little tricky, but all the JavaScript logic is taken care of for you in the code if you would like to do something similar to your dataset. The overall SCI score is outlined in gray (demonstrating how to change column background color) and logic to highlight scores below a threshold (with bold and red font) is also overviewed in code.
Table 2: Metric scores used to calculate the VSCI for Catawba Creek. Orange shaded values indicate the two lowest scores that are most likely driving low VSCI scores. Red VSCI scores are below the impairment threshold of 60.
You can include some basic plots of all the ambient data you have collected throughout the input dataset. You can also use inline text again to overview the data automatically. E.g. Catawba Creek was sampled for pH data between 2016 and 2016. pH measurements in Catawba Creek ranged from 6.91 - 8.36 with a median value of 8.12 (n=14) (Figure 2).
Code for a basic ggplot is included. If you have cutoffs for certain parameters, you can go in to the parameter settings section to play with the background colors and zones. Note the difference between the pH plot and the DO plot. The background thresholds add a lot of context if you can add them.
Figure 2: Describe pH data here.
Dissolved Oxygen in Catawba Creek ranged from 8.91 - 14.45 mg/L. The median dissolved oxygen value was 11.42 mg/L (n=14) (Figure 3).
Figure 3: Describe DO data here.
If you are planning on distributing report as an HTML document, I recommend using interactive plots with the plotly package. The plots stay interactive even as you distribute the .html file. I find if very useful for larger datasets where users may want to zoom in/out of certain areas and especially with time series data. Below is an example with the total habitat scores.
Catawba Creek total habitat scores ranged from 146 - 147 and the median value was 146.5(n=2) (Figure 7).
Figure 7: Describe Total Habitat data here.
Further analysis of the the individual metrics (scored 1-20) that make up the qualitative total habitat score are visible in the heatmap below (Table 2). Darker colors indicate lower habitat metric scores.
Table 2: Qualitative habitat measurements taken during biological monitoring. The lighter red colors represent habitat scores that are optimal or suboptimal and progressively darker red colors represent marginal or poor habitat scores.